Early-Stage Biorefinery Design and Development Through a Multi-Scale Approach

Abstract

Biorefineries promise to replace linear and fossil-based production by leveraging renewable resources. Yet their complexity arises from technical uncertainties, economic constraints, and a variety of process options. This thesis introduces a multi-scale methodology for early-stage biorefinery design based on superstructure optimization with the OUTDOOR framework. By integrating genome-scale metabolic models into superstructure optimization, the method identifies promising microbial and substrate combinations under data-scarce conditions. Reactor-scale optimization, tested on a co-culture for propionate production, addresses trade-offs between yield and productivity and accounts for stochastic uncertainties. At the process scale, the approach manages variable feedstock compositions, fluctuating market prices, and emerging technologies.